2021
Frantzolas, Christos
Electrical Network Frequency Recording using a Raspberry Pi 3, Model B Technical Report
Department of Electrical and Computer Engineering University of Patras, no. DSIP/TR001, 2021.
Abstract | Links | BibTeX | Tags: Complex Trace Analysis, ENF, Python, Raspberry Pi
@techreport{Frantzolas2021,
title = {Electrical Network Frequency Recording using a Raspberry Pi 3, Model B},
author = {Christos Frantzolas},
url = {http://dsip.ece.upatras.gr/wp-content/uploads/2021/03/DSIP-TR001-10.03.2021.pdf},
year = {2021},
date = {2021-03-10},
number = {DSIP/TR001},
address = {University of Patras},
institution = {Department of Electrical and Computer Engineering},
abstract = {The Electrical Network Frequency (ENF) Criterion is a forensic technique used to identify the authenticity of a digital recording. When using this method, frequency changes are compared between the background utility hum in the evidence and long-term records of the ENF. Recording this frequency (also called mains frequency or power line frequency) can be performed with the usage of a Raspberry Pi - a small single-board computer. The device’s low cost and portability present a great advantage, but the limited computational power and storage capacity create unique problems on how to compute and store the ENF recordings. In this report, a solution is presented, in which the utility signal is first recorded through the audio port of the device, and the EN instantaneous frequency is computed using the Hilbert transform.},
keywords = {Complex Trace Analysis, ENF, Python, Raspberry Pi},
pubstate = {published},
tppubtype = {techreport}
}
The Electrical Network Frequency (ENF) Criterion is a forensic technique used to identify the authenticity of a digital recording. When using this method, frequency changes are compared between the background utility hum in the evidence and long-term records of the ENF. Recording this frequency (also called mains frequency or power line frequency) can be performed with the usage of a Raspberry Pi - a small single-board computer. The device’s low cost and portability present a great advantage, but the limited computational power and storage capacity create unique problems on how to compute and store the ENF recordings. In this report, a solution is presented, in which the utility signal is first recorded through the audio port of the device, and the EN instantaneous frequency is computed using the Hilbert transform.